| 引用本文: | 杨志豪,尤浏辉,朱少杰,刘皓明,王健.大数据产业园多类型楼宇群电能共享优化运行策略[J].电力自动化设备,2026,46(2):215-224. |
| YANG Zhihao,YOU Liuhui,ZHU Shaojie,LIU Haoming,WANG Jian.Optimal operation strategy of electric power sharing among multi-type building groups in a big data industrial park[J].Electric Power Automation Equipment,2026,46(2):215-224. |
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| 摘要: |
| 为了兼顾大数据产业园内、外的生产生活等多元化需求和用电经济性，亟需挖掘园区内不同类型楼宇群之间的互补潜力。为此，提出大数据产业园内多类型楼宇群的电能共享优化运行策略。给出大数据产业园内多类型楼宇群的电能共享架构；构建不同类型楼宇群的自趋优响应优化模型；建立基于非对称Nash谈判的多类型楼宇群电能共享优化运行策略，并将其转化为易于求解的两阶段序贯优化子问题；采用基于增强型自适应预测-校正的交替方向乘子法对两阶段序贯优化子问题进行分布式求解。算例结果表明，所提大数据产业园多类型楼宇群电能共享优化运行策略能够充分发挥不同楼宇群的资源互补优势，有效降低园区的总运行成本；高效的分布式求解算法能在保护楼宇群隐私的同时，保证电能共享交易的公平性。 |
| 关键词: 大数据产业园 楼宇群 电能共享 数据中心 非对称Nash谈判 优化运行 交替方向乘子法 |
| DOI：10.16081/j.epae.202507010 |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(52207091) |
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| Optimal operation strategy of electric power sharing among multi-type building groups in a big data industrial park |
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YANG Zhihao1, YOU Liuhui1, ZHU Shaojie2, LIU Haoming3, WANG Jian3
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1.College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, China;2.China Electric Power Research Institute, Nanjing 210003, China;3.School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
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| Abstract: |
| In order to balance the diverse demands for production and living both inside and outside the big data industrial park and to ensure the economic efficiency of electric power consumption, it is urgent to explore the complementary potential among different types of building groups within the park. Therefore, an optimal operation strategy of electric power sharing among multi-type building groups in the big data industrial park is proposed. The electric power sharing framework for multi-type building groups in the big data industrial park is presented. The self-optimizing response optimization model for different types of building groups is constructed. An optimal operation strategy of electric power sharing among multi-type building groups based on asymmetric Nash bargaining is proposed, which is transformed into two-stage sequential optimization sub-problems to be solved easily. The enhanced adaptive prediction-correction-based alternating direction multiplier method is adopted to solve the two-stage sequential optimization sub-problems in a distributed fashion. The results of case study show that the proposed optimal operation strategy of electric power sharing among multi-type building groups in the big data industrial park can fully leverage the complementary advantages of different building groups and effectively reduce the total operation cost of the park. The efficient distributed solution algorithm can protect the privacy of building groups while ensuring the fairness of electric power sharing transactions. |
| Key words: big data industrial park building groups electric power sharing data center asymmetric Nash bargaining optimal operation alternating direction multiplier method |
